Performance Investigation for Medical Image Evaluation and Diagnosis Using Machine-Learning and Deep-Learning Techniques
Today, medical image-based diagnosis has advanced significantly in the world. The number of studies being conducted in this field is enormous, and they are producing findings with a significant impact on humanity. The number of databases created in this field is skyrocketing. Examining these data is...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-03-01
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| Series: | Computation |
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| Online Access: | https://www.mdpi.com/2079-3197/11/3/63 |
| _version_ | 1797612596602339328 |
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| author | Baidaa Mutasher Rashed Nirvana Popescu |
| author_facet | Baidaa Mutasher Rashed Nirvana Popescu |
| author_sort | Baidaa Mutasher Rashed |
| collection | DOAJ |
| description | Today, medical image-based diagnosis has advanced significantly in the world. The number of studies being conducted in this field is enormous, and they are producing findings with a significant impact on humanity. The number of databases created in this field is skyrocketing. Examining these data is crucial to find important underlying patterns. Classification is an effective method for identifying these patterns. This work proposes a deep investigation and analysis to evaluate and diagnose medical image data using various classification methods and to critically evaluate these methods’ effectiveness. The classification methods utilized include machine-learning (ML) algorithms like artificial neural networks (ANN), support vector machine (SVM), k-nearest neighbor (KNN), decision tree (DT), random forest (RF), Naïve Bayes (NB), logistic regression (LR), random subspace (RS), fuzzy logic and a convolution neural network (CNN) model of deep learning (DL). We applied these methods to two types of datasets: chest X-ray datasets to classify lung images into normal and abnormal, and melanoma skin cancer dermoscopy datasets to classify skin lesions into benign and malignant. This work aims to present a model that aids in investigating and assessing the effectiveness of ML approaches and DL using CNN in classifying the medical databases and comparing these methods to identify the most robust ones that produce the best performance in diagnosis. Our results have shown that the used classification algorithms have good results in terms of performance measures. |
| first_indexed | 2024-03-11T06:44:16Z |
| format | Article |
| id | doaj.art-f028c3a52607432986c5e446889abeed |
| institution | Directory Open Access Journal |
| issn | 2079-3197 |
| language | English |
| last_indexed | 2024-03-11T06:44:16Z |
| publishDate | 2023-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Computation |
| spelling | doaj.art-f028c3a52607432986c5e446889abeed2023-11-17T10:26:16ZengMDPI AGComputation2079-31972023-03-011136310.3390/computation11030063Performance Investigation for Medical Image Evaluation and Diagnosis Using Machine-Learning and Deep-Learning TechniquesBaidaa Mutasher Rashed0Nirvana Popescu1Computer Science Department, University POLITEHNICA of Bucharest, 060042 Bucharest, RomaniaComputer Science Department, University POLITEHNICA of Bucharest, 060042 Bucharest, RomaniaToday, medical image-based diagnosis has advanced significantly in the world. The number of studies being conducted in this field is enormous, and they are producing findings with a significant impact on humanity. The number of databases created in this field is skyrocketing. Examining these data is crucial to find important underlying patterns. Classification is an effective method for identifying these patterns. This work proposes a deep investigation and analysis to evaluate and diagnose medical image data using various classification methods and to critically evaluate these methods’ effectiveness. The classification methods utilized include machine-learning (ML) algorithms like artificial neural networks (ANN), support vector machine (SVM), k-nearest neighbor (KNN), decision tree (DT), random forest (RF), Naïve Bayes (NB), logistic regression (LR), random subspace (RS), fuzzy logic and a convolution neural network (CNN) model of deep learning (DL). We applied these methods to two types of datasets: chest X-ray datasets to classify lung images into normal and abnormal, and melanoma skin cancer dermoscopy datasets to classify skin lesions into benign and malignant. This work aims to present a model that aids in investigating and assessing the effectiveness of ML approaches and DL using CNN in classifying the medical databases and comparing these methods to identify the most robust ones that produce the best performance in diagnosis. Our results have shown that the used classification algorithms have good results in terms of performance measures.https://www.mdpi.com/2079-3197/11/3/63medical image dataset analysisdiagnosismachine learningdeep learning |
| spellingShingle | Baidaa Mutasher Rashed Nirvana Popescu Performance Investigation for Medical Image Evaluation and Diagnosis Using Machine-Learning and Deep-Learning Techniques Computation medical image dataset analysis diagnosis machine learning deep learning |
| title | Performance Investigation for Medical Image Evaluation and Diagnosis Using Machine-Learning and Deep-Learning Techniques |
| title_full | Performance Investigation for Medical Image Evaluation and Diagnosis Using Machine-Learning and Deep-Learning Techniques |
| title_fullStr | Performance Investigation for Medical Image Evaluation and Diagnosis Using Machine-Learning and Deep-Learning Techniques |
| title_full_unstemmed | Performance Investigation for Medical Image Evaluation and Diagnosis Using Machine-Learning and Deep-Learning Techniques |
| title_short | Performance Investigation for Medical Image Evaluation and Diagnosis Using Machine-Learning and Deep-Learning Techniques |
| title_sort | performance investigation for medical image evaluation and diagnosis using machine learning and deep learning techniques |
| topic | medical image dataset analysis diagnosis machine learning deep learning |
| url | https://www.mdpi.com/2079-3197/11/3/63 |
| work_keys_str_mv | AT baidaamutasherrashed performanceinvestigationformedicalimageevaluationanddiagnosisusingmachinelearninganddeeplearningtechniques AT nirvanapopescu performanceinvestigationformedicalimageevaluationanddiagnosisusingmachinelearninganddeeplearningtechniques |